ADHD symptoms are associated with the modular structure of intrinsic brain networks in a representative sample of healthy adults

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only 8 out of 291 participants scored in the clinical range. Subject-specific brain network graphs were modeled from functional MRI resting-state data and revealed significant associations between (nonclinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks.

Neurophysiological markers of ADHD symptoms in typically-developing children

“…A more dimensional conceptualization of ADHD was recently proposed 10 . Empirical research supporting this proposal demonstrated continuous covariations between ADHD symptoms in healthy individuals on the one hand and behavioral 11 , 13 or neural 16 characteristics on the other hand. Importantly, all neurophysiological associations reported in the present study were also observed across a continuous and broad range of individual variations.…”

“…However, these approaches ignore that symptoms vary substantially between affected persons 9 – 12 and even subclinical variations in ADHD symptoms significantly impact cognitive functioning and psychological wellbeing 13 . This suggests that the investigation of continuous variations in ADHD-related behavior can provide insights into whether or not neurocognitive models of ADHD (like the default-mode interference hypothesis 14 , or the cognitive-energetic model 5 , 6 , 15 ) can be generalized to explain behavioral variations in attention and executive functions within the general population 16 .…”

Neural primacy of the dorsolateral prefrontal cortex in patients with obsessive-compulsive disorder

“…This method brings about a great reduction in motion-induced artifacts when combined with various motion correction strategies ( Power et al, 2013 , Satterthwaite et al, 2013 ). This threshold has been widely used in recent studies to rigorously control head motion ( Fukushima et al, 2018 , Hilger and Fiebach, 2019 , Yan et al, 2019 ). Mean FD values were calculated from translational and rotational scan-to-scan displacements using three translational parameters and three rotational parameters obtained from realignment steps for each subject ( Power et al, 2013 ).…”

BrainNET: Inference of Brain Network Topology Using Machine Learning

“…The real-world performance of BrainNET was then evaluated in a publicly available data set of ADHD. ADHD is one of the most common neurodevelopmental disorders in children with significant socioeconomic and psychological effects (Hilger and Fiebach, 2019;Lin et al, 2014). It can be difficult to diagnose due to the overlapping nature of symptoms, with resultant diagnostic errors and overprescribing of medications due to misdiagnosis (Saeed, 2018).…”